B-Spline Neural Networks Based PID Controller for Hammerstein Systems
- Cite this paper as:
- Hong X., Iplikci S., Chen S., Warwick K. (2012) B-Spline Neural Networks Based PID Controller for Hammerstein Systems. In: Huang DS., Gupta P., Zhang X., Premaratne P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg
A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.
KeywordsHammerstein model PID controller system identification
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